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Type:	Artigo de Periódico
Title:	A new fault classification approach applied to Tennessee Eastman benchmark process
Authors:	Marcos Flávio Silveira Vasconcelos D"Angelo Reinaldo Martinez Palhares Murilo César Osório Camargos Renato Dourado Maia João Batista Mendes Petr Iakovlevitch Ekel
Abstract:	This study presents a data-based methodology for fault detection and isolation in dynamic systems based on fuzzy/Bayesian approach for change point detection associated with a hybrid immune/neural formulation for pattern classification applied to the Tennessee Eastman benchmark process. The fault is detected when a change occurs in the signals from the sensors and classified into one of the classes by the immune/neural formulation. The change point detection system is based on fuzzy set theory associated with the Metropolis–Hastings algorithm and the classification system, the main contribution of this paper is based on a representation which combines the ClonALG algorithm with the Kohonen neural network.
Subject:	Engenharia elétrica Algoritmos Redes neurais (Computação) Benchmarking (Administração)
language:	eng
metadata.dc.publisher.country:	Brasil
Publisher:	Universidade Federal de Minas Gerais
Publisher Initials:	UFMG
metadata.dc.publisher.department:	ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
Rights:	Acesso Aberto
metadata.dc.identifier.doi:	https://doi.org/10.1016/j.asoc.2016.08.040
URI:	http://hdl.handle.net/1843/42874
Issue Date:	Dec-2016
metadata.dc.url.externa:	https://www.sciencedirect.com/science/article/pii/S1568494616304343#!
metadata.dc.relation.ispartof:	Applied Soft Computing
Appears in Collections:	Artigo de Periódico

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